Real-time image processing algorithms for an automated retinal laser surgery system

Although laser retinal surgery is the best available treatment for a variety of ophthalmic disorders, the current procedure has a success rate of less than 50%. A computer-controlled instrument is under development for minimizing this failure rate. This paper presents core image-processing methods used for this system. The image-processing problems of interest in this paper are (i) the computation of a retinal map from video retinal angiograms, and (ii) real-time eye tracking and location determination during laser surgery. The algorithms described here comprise of a fast method for detecting retinal landmark feature points, a fast method for matching feature-tagged point sets, feature-based method for montaging retinal angiograms, and an algorithm for real-time image tracking